Understanding the Why: Modeling Motive in Incidents of Violence Against Aid Workers



Despite their neutrality and inherent nobel intentions, aid workers are often subjected to horrible and unprecedented violence when performing live-saving work. They are constantly putting themselves in harm’s way to ensure the safety of others, providing humanitarian relief to those most in need. However, these efforts are often thwarted by targeted attacks that exploit their vulnerability. The question raised is why, why do these horrific acts happen to these innocent aid workers. Understanding this question is crucial to improving their safety and security so that they are able to provide the relief efforts that so many rely on.

To delve into this question, we will work with the Aid Worker Security Database (AWSD). This comprehensive database relies on aid worker accounts and after the fact investigations to compile attacks on humanitarian workers across the world. AWSD is an on-going research project providing information on attacks dating back to 1997 with information ranging from the attack location, to the perpertrator, to the number of people impacted. Importantly for this research, it includes the reason behind the attack or the motive. These are broken into six distinct categories.

We will exploit these motive categories to perform analysis on the attacks, focusing on how the motive fits into the bigger picture and whether understanding the motive can create informed strategies to improve the safety and security of aid workers.

The angle of this research is whether the motive of the attacks shifts the outcome or the group of people impacted? To answer this, we will investigate multiple subquestions:

  1. How are the motives changing over time?
  2. Are certain motives more common in certain areas?
  3. Is the type of motive linked to the outcome?
    1. Types of attacks?
    2. Certain motives more deadly?
  4. Are certain groups more at risk based on the motive?
    1. National vs international?
    2. Type of Agency?

We now dig into each of these subquestion to better understand what is driving these attacks.


Breaking Down the Motive



Insights: We have tried to pinpoint global trends in the motives behind the aid worker attacks. We will now look back to our initial guiding questions to summarize the main findings.

  1. How are the motives changing over time?
    • General upward trend of attacks (ignoring 2020)
    • Incidental skyrocketing from 2021 to 2024
  2. Are certain motives more common in certain areas?
    • Attacks are centralized in Africa, the Middle East, and the Pacific Islands
    • Political attacks most common in Afghanistan
    • Incidental attacks most common in South Sudan and Afghanistan
    • Economic attacks most common in South Sudan
  3. Is the type of motive linked to the outcome?
    • Political attacks lead to most injuries overall
    • Incidental attacks lead mostly to killings
    • Economic attacks lead mostly to woundings
  4. Are certain groups more at risk based on the motive? (Type of Agency)
    • Political attacks impact ICRC, NNGO, and the UN
    • Economic attacks impact NNGO and the UN
    • Incidental attacks impact NRCS & IFRC
  5. Are certain groups more at risk based on the motive? (National vs international)
    • Nationals are involved in all types of attacks more than internationals
    • Both nationals and internationals mainly injured in political motivated attacks
    • Most killings of nationals result from political or incidental attacks
    • Most woundings of nationals result from economic attacks
    • Most kidnappings of internationals result from political attacks


Recommendations

With a better understanding of the landscape of attacks, particularly the motivation behind them, we were able to see how the outcomes of the attacks may shift as well as the types of people impacted. The emerging trends allow us to develop some actionable insights that could improve the safety and security of aid workers.

Of course, it is important to note that these attacks are quite sensitive and that we are looking only at the attacks that happened, not those that were attempted or were simply not recorded. In addition, stopping these types of attacks is much more involved than simply making small changes to security as they occur due to deeply engrained societal issues. While the priority is, without a doubt, keeping aid workers safe and allowing them to provide humanitarian relief to people in need, there are never any guarantees, especially in the charged and complicated situations they are entering.

That being said, certain trends offer insights that could inform changes, however small, that may enhance aid worker safety or help reduce the number of attacks. The main recommendation is to identify regions as different motivation hot zones based on the political, economic, and overall climate of the area. Knowing this beforehand can allow for proper preparation and keep aid agencies vigilant of the shifting threat of attack. With designated hot zones, they can shift their attention, resources, and training in the most effective manner.

It seems pertinent to focus much of the safety measures and resources in the regions of the Middle East and Africa, maybe even homing in on Sudan and Afghanistan. Again, it is important to note that there is a complex political climate within these countries making it difficult to impact the root of the problem. However, having more protection and allocating monitoring and preventative resources in these regions may improve the overall safety and preparedness of aid workers.

We have found that politics are overwhelming the main cause of these attacks. To combat this fact, aid worker organizations should be vigilant about understanding the political climate of countries they may be working in and identifying hot-spots regions. In addition, governments are well aware of politically charged regions of the world and should be prioritizing diplomatic and humanitarian engagement in these regions in an effort to stabilize the political climate, which could, in effect, decrease the number of attacks on aid workers.

In terms of the breakdown of which attack outcomes are most prevalent with specific motives, this could help with the type of response being allocated. For example, first-aid logistics should be tailored to the specific type of region (i.e. a country with a tense economic climate). Having first aid and emergency response teams at the ready in all politically charged hot zones could get aid workers to safety much faster. Protective security in incidental zones may decrease the number of killings.

The training received by the different aid organizations and nationals versus internationals could be tailored based on what attack they are most likely to encounter. Protective training should be provided for nationals in political hot zones, while medical preparedness for woundings and minor injuries should be taught for nationals entering economic zones. Kidnapping training and protocol should be implemented for international staff in political hotspots.

Unfortunately, each and everyday aid workers will continue to put themselves in imminent danger. The hope is that analyzing the data on these horrific attacks can provide some additional support to the aid workers and begin prioritizing the important work they are doing.









References

  1. “Aid Worker Security Database: Aid Worker Security Database.” Aid Worker Security Database | Aid Worker Security Database, www.aidworkersecurity.org/. Accessed 24 Mar. 2025.
  2. “Aid Worker Security Database (AWSD) Codebook.” Aid Worker Security Database (AWSD) Codebook | Humanitarian Outcomes, 1 Feb. 2025, humanitarianoutcomes.org/AWSD-codebook.
  3. Plot Libraries Reference Galleries:
    1. https://plotly.com/ggplot2/
    2. https://r-graph-gallery.com/
    3. https://gganimate.com/
    4. https://www.rdocumentation.org/packages/plotly/versions/4.10.4/topics/plot_geo
  4. AI Usage (ChatGPT):
    1. Help with fixing some closeread formatting issues
    2. Creating the first choropleth, specifically using plot_geo
    3. Summing the events by agency and motive for the pie charts
    4. Fixing the theme issues for the pie charts
    5. Pivoting Long for bar charts, pie charts, and heatmaps
    6. Aggregating for the heatmap
    7. Overall R errors and debugging help throughout


Data Preprocessing & Plotting Code